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Urgent! DBiz.ai - Machine Learning Engineer - AWS & MLOps Job Opening In Kochi – Now Hiring DBiz.ai

DBiz.ai Machine Learning Engineer AWS & MLOps



Job description

<p><p><b>Job Summary :</b></p><p><p><b><br/></b></p>We are seeking a highly skilled and motivated Machine Learning Engineer with a strong foundation in programming and machine learning, hands-on experience with AWS Machine Learning services (especially SageMaker), and a solid understanding of Data Engineering and MLOps practices.<br/><br/></p><p>You will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native Responsibilities : </b></p><p><br/></p>- Design and implement machine learning models and pipelines using AWS SageMaker and related services.<br/><br/></p><p>- Develop and maintain robust data pipelines for training and inference workflows.<br/><br/></p><p>- Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions.<br/><br/></p><p>- Implement MLOps best practices including CI/CD for ML, model versioning, monitoring, and retraining strategies.<br/><br/></p><p>- Optimize model performance and ensure scalability and reliability in production environments.<br/><br/></p><p>- Monitor deployed models for drift, performance degradation, and anomalies.<br/><br/></p><p>- Document processes, architectures, and workflows for reproducibility and Skills & Qualifications : </b></p><br/></p><p>- Strong programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).<br/><br/></p><p>- Solid understanding of machine learning algorithms, model evaluation, and tuning.<br/><br/></p><p>- Hands-on experience with AWS ML services, especially SageMaker, S3, Lambda, Step Functions, and CloudWatch.<br/><br/></p><p>- Experience with data engineering tools (e.g., Apache Airflow, Spark, Glue) and workflow orchestration.<br/><br/></p><p>- Proficiency in MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes).<br/><br/></p><p>- Familiarity with monitoring tools and logging frameworks for ML systems.<br/><br/></p><p>- Excellent problem-solving and communication Qualifications : </b></p><p><br/></p>- AWS Certification (e.g., AWS Certified Machine Learning Specialty).<br/><br/></p><p>- Experience with real-time inference and streaming data.<br/><br/></p><p>- Knowledge of data governance, security, and compliance in ML systems</p><br/></p> (ref:hirist.tech)


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